Short-Term Fiscal Projections Using Forecast Combination Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper the author compares a number of methods to forecast corporate income tax revenues in the next quarter: autoregressive integrated moving average, exponential smoothing, linear regression, naïve forecast, and combination approaches. Results show that the increase of the time period for the next quarter forecast error calculation leads to the increase of errors for individual models and arithmetic mean, and the decrease for combination approaches which consider forecast errors in previous periods. Linear regression with the MOEX Russia Index as explanatory variable provides the lowest error for the next quarter forecast for 5- and 10-quarter periods. The forecast combination approach, what takes into account the forecast error in previous 4 quarters, provides the best result for a period of 15 quarters, what is explained by diversification of forecast errors. This method can be successfully used for corporate income tax revenue projections and possibly for other budget revenues.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it